...(BHRM) BUSINESS STATISTICS (BBI 1224) Name : Student ID# : Semester : Academic Honesty Policy Statement I, hereby attest that contents of this attachment are my own work. Referenced works, articles, art, programs, papers or parts thereof are acknowledged at the end of this paper. This includes data excerpted from CD-ROMs, the Internet, other private networks, and other people’s disk of the computer system. Student’s Signature : | |for office use only | |SUPERVISOR’S COMMMENTS/GRADE: | | | | | | |DATE : ------------------------ | | | | | |TIME : ________________ | | | | | ...
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...Revere Street Working Paper Series Financial Economics 272-18 Mean-Variance Analysis versus Full-Scale Optimization Out of Sample First Version: November 11, 2005 This Draft: December 13, 2005 Timothy Adler Windham Capital Management, LLC 5 Revere Street Cambridge, MA 02138 617 234-9459 tadler@windhamcapital.com Abstract For three decades, mean-variance analysis has served as the standard procedure for constructing portfolios. Recently, investors have experimented with a new optimization procedure, called full-scale optimization, to address certain limitations of mean-variance optimization. Specifically, mean-variance optimization assumes that returns are normally distributed or that investor preferences are well approximated by mean and variance. Full-scale optimization relies on sophisticated search algorithms to identify the optimal portfolio given any set of return distributions and based on any description of investor preferences. Full-scale optimization yields the truly optimal portfolio in sample, whereas the mean-variance solution is an approximation to the insample truth. Both approaches to portfolio formation, however, suffer from estimation error. Mean-variance analysis requires investors to estimate the means and variances of all assets and the covariances of all asset pairs. To the extent the out-of-sample experience of these parameters departs from the in-sample parameter values, the mean-variance approximation will be even less accurate. Full-scale optimization...
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...Revere Street Working Paper Series Financial Economics 272-18 Mean-Variance Analysis versus Full-Scale Optimization Out of Sample First Version: November 11, 2005 This Draft: December 13, 2005 Timothy Adler Windham Capital Management, LLC 5 Revere Street Cambridge, MA 02138 617 234-9459 tadler@windhamcapital.com Abstract For three decades, mean-variance analysis has served as the standard procedure for constructing portfolios. Recently, investors have experimented with a new optimization procedure, called full-scale optimization, to address certain limitations of mean-variance optimization. Specifically, mean-variance optimization assumes that returns are normally distributed or that investor preferences are well approximated by mean and variance. Full-scale optimization relies on sophisticated search algorithms to identify the optimal portfolio given any set of return distributions and based on any description of investor preferences. Full-scale optimization yields the truly optimal portfolio in sample, whereas the mean-variance solution is an approximation to the insample truth. Both approaches to portfolio formation, however, suffer from estimation error. Mean-variance analysis requires investors to estimate the means and variances of all assets and the covariances of all asset pairs. To the extent the out-of-sample experience of these parameters departs from the in-sample parameter values, the mean-variance approximation will be even less accurate. Full-scale optimization...
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...American InterContinental University Abstract In this paper for the Unit 1 IP it will talk about a study for American Intellectual Union (AIU). In this paper it will talk about the examined data and the results. Also the paper talks about qualitative data and quantitative data. Introduction The Unit 1 Individual project states a scenario. This scenario is that American Intellectual Union (AIU) has assembled a team of researchers in the United States and around the world to study job satisfaction. Also with the scenario we are to write about the study and the findings from the data of the research. The results will allow managers all over to be able to have job satisfaction within the companies no matter what size. Chosen Variables The first variable that was chosen to analyze is Gender for the qualitative variable. This is so because it would give a better view on why someone does or does not have job satisfaction and if it is because of the gender of the individual. “Qualitative variable is when observations cannot be described meaningfully in terms of numbers.” (Qualitative variable and quantitative variable, 2000) For quantitative variable Intrinsic was chosen. “A quantitative variable is observations that can be characterized in numerical terms.” (Qualitative variable and quantitative variable, 2000) Difference in variable types There is a difference between qualitative and quantitative variables. Qualitative data is data that you are not able to...
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...Running Head: Fundamentals of Statistics Abstract This paper provides an analysis of a small sample on the recent company job satisfaction survey. The focus of the study includes one qualitative data set (company position) and one quantitative data set (intrinsic). To support the analysis the following are also included in this paper: reason why these particular data sets were selected, calculations used, reason why some statistical measures did not apply to certain data sets, and what was learned from the analysis. Additionally, a graphical depiction to support data calculations and a conclusion is included. Introduction A job satisfaction survey is an analysis of employees who are satisfied with their job and the duties they perform. The proceedings of this paper include a qualitative and quantitative data collected during a job satisfaction survey. Quantitative data are data values that are numeric. Whereas, qualitative data are data values that can be place into distinct categories according to some of their character tics or attributes. The study serves as a foundation for future analysis in an effort to make accurate conclusions in regards to understanding global job satisfaction. A logical explanation for selecting the data sets analyzed as well as what was derived from the selected data sets will be provided. Qualitative Data: Company Position For this analysis I have chosen to use the company position data set for my qualitative assessment...
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...Week 3 E-Text Chauntilena Goodwin RES/342 May 1, 2012 Olivia Scott Week 3 E-Text 10.30 In Dallas, some fire trucks were painted yellow (instead of red) to heighten their visibility. During a test period, the fleet of red fire trucks made 153,348 runs and had 20 accidents, while the fleet of yellow fire trucks made 135,035 runs and had 4 accidents. At α = .01, did the yellow fire trucks have a significantly lower accident rate? (a) State the hypotheses. H0: p1 ≥ p2 H1: p1 < p2 (b) State the decision rule and sketch it. Reject the null hypothesis if the critical value is less than z.010= -2.326 and the p-value is greater than .01. (c) Find the sample proportions and z test statistic. P1 = 4135,035 = .00002962 p2 = 20153,348 = .00013042 P = x1+ x2n1+ n2 = 4+20135,035+153,348 = .00008322 z.010 = P1-P2p1-P[1n1+1n2] = .00002962-.00013042.000083221-.0000832[1135,035+1153,348] = -2.9610 (d) Make a decision. Since the z.010 < -2.326 the decision is to reject the null hypothesis (e) Find the p-value and interpret it. P (z<-2.9610) = .0015. The probabilities of a z-score lower than -2.9610 is only .0015. (f ) If statistically significant, do you think the difference is large enough to be important? If so, to whom, and why? The difference of statistical significant is important so that the fire department can have information that can lower cost and make operation safer. (g) Is the normality assumption fulfilled? Explain. The sample size is large...
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...team member’s descriptive statistics for week four incorporating the best elements into one team data analysis paper, including data and charts. Descriptive Statistics The research topic for this paper is “McDonald’s is Closing Hundreds of Stores.” As part of the research a sample size of 400 was selected to ensure accuracy of results based on the population size of 410. The given sample size was randomly surveyed to test the variables – Independent Variable - Change in consumer food preference and competitive market place and Dependent Variable – Reduced sales hence reduced profit. Age Three Hundred and Eighty-Five McDonald’s consumers were randomly selected and their ages measured. The age ranges were 15 and 65 years. Average consumer is aged 31 with a standard deviation of 14 years. Approximately half or more of their ages are above 31. Income The income of the randomly surveyed consumers is averaged at $30.82 and with a standard deviation of $14.04. Income range is $15 to $65 and there is enough evidence that half or more of these consumers averages $30.82 per year. Strengths and Weaknesses of Team Members’ Individual Assignments Efforts were made by each team member to better understand the use of the statistical tool made available to us (MegaStat). More knowledge was gained and applied in the interpretation of data and findings on this paper. Additionally, each team member pulled their weight in the interpretation and findings in the completion of...
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...Unit 1 - Fundamentals of Statistics Patricia Schneider American InterContinental University Abstract This paper is about the difference between qualitative data and quantitative data. If also will show how a qualitative data chart looks like and how the information is retrieved, it shows what type of information is put in a quantitative chart and how it is also retrieved. What standard deviation and variance is? Why charts and graphs are important tool for communicating facts and figures? Introduction The data that I chose for the qualitative data was the gender, and the quantitative data that I chose was the intrinsic. In this essay you will learn what the differences between qualitative data and quantitative data is? Why graphs and charts are so important in businesses and why they are used in communicating the facts? Chosen Variables The data that I have chosen is the gender and the intrinsic. The gender is qualitative data and the intrinsic is the quantitative data. Difference in variable types The difference between qualitative and quantitative variables is the qualitative has no value or is just a label where the quantitative has a value. The qualitative is a label there is no value of information to be measured. Qualitative is a non numerical measurement on a set of people or objects (Segal, 2011). Quantitative is numerical measurement for a set of people or objects (Segal, 2011). Descriptive statistics: Qualitative variable | | Qualitative by Gender...
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...Sample Hypothesis Testing Paper RES/342 Two-Sample Hypothesis Research Question A company decides to purchase homes to rent out to their employees. They have to decide if purchasing homes in a rural area is cheaper than purchasing homes from the inner city. 15 miles from the center of the city is inner city and anything passed 15 miles is rural area. The mean house pricing in the urban area is $232,736 out of a sample size of 59 with a standard deviation of $48,651. The mean house pricing in the rural area is $206,183 out of a sample size of 46 with a standard deviation of $40,896. At the 0.05 significance level, the company assumes that urban area is higher than rural area. Formulating a Verbal and Numeric Hypothesis To formulate a verbal and numerical hypothesis statement one must determine which type of two-sample statistic test the problem describes. The statement indicates that one would perform a right-tailed test. The words “is higher than” indicates that the alternate hypothesis will have the greater than sign in it. The statement also indicates that the standard deviation is known. Using this knowledge one may begin to formulate a verbal hypothesis. The researchers will test the hypothesis statement and try to find sufficient evidence to support the null hypothesis. If the evidence is insufficient one will reject the null hypothesis. If the evidence is sufficient one may fail to reject the null hypothesis. Simply put one is trying to figure out if the company...
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...this paper is to use APT framework to investigate both the existing and pricing questions. The _rst test in the paper shows that equity returns seem to depend on several common factor, perhaps as many as four. Table 3 explains this statement. When _0 is assumed to be 6%, 88:1% of the groups had at least one signi_cant factor risk premium, 57:1% had at least two signi_cant factor risk premium and 33:3% of the groups at least three risk premia were signi_cant. However, this results are far from the case in which _0 = 0. So, part 1 of table 3 shows that at least three factors are important for pricing but it is unlikely that more than four are present. In the second part of table 3, _0 is not assumed to be a constant, on the contrary it is estimated. However, this does not change the result much, similar statistics are obtained. 47:6% of the groups had at least two signi_cant factor risk premium while only 7:1% of the groups had three or more signi_cant factor risk premium. The three signi_cant fac- tors which is obtained when _0 = 6% may be an overestimate because of the incorrect choice of the zero beta return _0. The second test is about one particular variable, the total variance of individual returns or the \own" variance. When APT is valid, total variance would not a_ect expected returns since its diversi_able component would be eliminated by portfolio formation and its non-diversi_able part would depend only on the factor loading and factor variances. However...
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...QUANTITATIVE METHODS AND ANALYSIS Fundamentals of Statistics Raymond Lawson AIU Online Virtual Campus Abstract This paper will discuss the analysis of two sets of data. The mean, median, and mode will be analyzed and charted. The standard deviation will also be discussed along with the variance. A summary of the data and its results will be discussed in conclusion. Fundamentals of Statistics Statistics are used for many different things in our lives. We have used them to in order to try and figure out who is the best at a sport or what company may give us the best return on our investment. The bottom line is statistics have become a part of our everyday life. We are going to use statistics to answer some questions we have about people and how they feel about their jobs. For this analysis we are looking at two fields of data. The first is the gender and the second is the extrinsic. We have decide to examine this data to help us understand the difference in how the two genders feel about where they work and how their feelings rate differently. Let’s first take a look at the gender of those surveyed. The mean of those surveyed by gender is mostly female. We will also look at the mean of the extrinsic job satisfaction. The scale goes from 1 being the least satisfied to 7 as the most satisfied. The mean shows that on average both genders are satisfied fairly equal to each other. Here are the charts. This shows a mean of 1.56667 which means that there were more females...
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...the median using the original method (paper and pencil), you have to arrange the values into numeric order (True/False). True 3. The interquartile range for this data is (round each value to 3 decimal places):_______. Lower quartile =(-0.1+0.5 )/2=0.200 Upper quartile =(6.5+7 )/2=6.750 Interquartile range = 6.75- 0.2 = 6.550 4. The formula for calculating the interquartile range is_____________ (show the formula...
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...94295=0.05705 95%=1-0.94295=0.05705 2.24 H0: M1=16 H1: M1=M2 |Machine 1 |Machine 2 | |Mean |16.015 |16.005 | |Variance |0.00082 |0.000585 | |Sample Number |10 |10 | | 2.25 H0: M1>M2 H1: M1>10 Z0= (162.5-155.0)/sq rt {(1.062/10) + (1.0^2/12)} =1.75, = 0.95994 No based on the Z-value, we will not be rejecting the null hypothesis. Hypothesis: The flight distance of the lighter paper airplane, M(g) is greater than the flight distance for the heavy paper airplane (with 2 paperclips) H0: M1 =M2 H1: M1> M2 Flight # |Light Paper Airplane Distance (ft) |Heavy Paper Airplane Distance (ft) | |1 |16 |8 | |2 |17 |8 | |3 |17.25 |8.25 | |4 |17.25 |8 | |5 |17 |7.75 | |6 |16.75 |7.75 | |7 |16.50 |7.75 | |8 |16.25 |8 | |9 |17 |8.25 | |10 |17.5 |8.25 | | Mean of Light Paper Airplane: = (16 + 17 +17.25 + 17.25 + 17 + 16.75 +16.50 +16.25 + 17 +17.5) 10 = 16.85 Mean of Heavy Paper Airplane: = ( 8 + 8 + 8.25 + 8 + 7.75 + 7.75 + 7.75 +8 + 8.25 + 8.25) 10 = 8 Variance of Light Paper Airplane: = [(16-16.85)^2 + (17--16.85 )^2 +(17.25-16.85)^2 + (17.25-16.85)^2 + (17 -16.85)^2 + (16.75 -16.85 )^2 +(16.50 -16.85 )^2 +(16.25 -16.85 )^2 + (17 -16.85 )^2 +(17.5 -16.85 )^2] 10 = 0.2025 Standard Deviation: = Sq Rt(0.2025) = 0.45 Variance of Heavy Paper...
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...this paper we will make the stock market analysis and commentary on the quotations of the Commercial Bank for the years 2007 and 2008 based on its prices in the Athens Stock Exchange. SECTION I PERFORMANCE AND AVERAGE YIELD The performance of a share is equal to the percentage difference between the initial and final property owner. The average yield value is calculated as the sum of the yield of a share to the number of returns. As far as the Commercial Bank is concerned, the average stock performance for the year 2007 was 0.28% and 2.24% for 2008. Based on the values of the general price index, the average yield for 2007 was 0.26% for 2008 and 1.95%. We observe that for the year 2007 by comparing the average yield to the corresponding share of the overall index the first appears in negative contrast to the second which is positive. By contrast, for the year 2008 there is identification -signs are in both negative- and price performances are quite close. Yields will then help us to calculate the beta value and to draw conclusions from them. VARIANCE The variance of a variable measures the volatility of the element. To calculate the variance four steps are required: • Calculation of average performance • Calculation of deviation from the mean square performance • Calculate the square of performance • Calculate the sum of the squares of the deviation and dividing by the number of observations. Regarding the variation in yields: • The variance of the...
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...In this paper, I will continue to work on A. Schulman, Inc (SHLM) along with its two biggest competitors PolyOne Corporation (POL) and Dow Chemical (DOW). Rate Of Return To calculate the rate of return for each of the firms for last three years, I used the adjusted yearly close price, which includes dividends and splits. ( please refer to Appendix I ). Clearly, the three companies were hurt by the financial crises. Especially in 2008, they all had losses. However, Schulman had lost the least compared to PolyOne, which was the biggest loser. After 2008 all three companies adjusted their position and gained profits. You can notice that PolyOne which recorded the highest losses in 2008, gained the highest returns in 2009 compared to the Dow and Schulman that had the least profits. In 2010, the three companies continued to earn profit but with a weaker momentum. Expected rate of return In order to estimate the rate of return for the following year, I have estimated the probability of having strong, normal, and weak demand. Since I believe that the market characteristics have somehow changed after the financial crises, I’ve based my estimates for the rate of return on the last 10 years with some adjustments to make it more related to after the recession period. (Refer to Appendix II) In my estimation of the expected rate of return, the three companies will slightly continue to grow profit. Of course that depends on how long it is going to take for the oil prices to drop, due...
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